# Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN

## TL;DR

This paper introduces a novel Direction-Fused FCN method for improved catheter segmentation and localization in 3D cardiac ultrasound, achieving significant accuracy gains over existing techniques.

## Contribution

The paper presents a new FCN architecture that leverages 3D information for better catheter segmentation in ultrasound images, enhancing detection accuracy.

## Key findings

- Achieved a Dice score of 57.7%, 11.8% higher than previous methods.
- Localized catheter with an average error of 1.4 mm.
- Successfully detected catheters in challenging ex-vivo 3D US datasets.

## Abstract

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8 % improvement when compared to state-of-the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.